Semi-Supervised Classification on Credit Card Fraud Detection using AutoEncoders

Nur Rachman Dzakiyullah, Andri Pramuntadi, Anni Karimatul Fauziyyah

Abstract


The use of credit cards for online purchases has increased dramatically and led to an explosion in credit card fraud. Credit card companies need to be able to identify fraudulent credit card transactions so that customers are not charged for items they do not buy. In this study, we will use semi-supervised learning and combine it with AutoEncoders to identify fraudulent credit card transactions. In this paper, we will implement the use of T-SNE to visualize fraud and non-fraud transactions, then improve the visualization using autoencoders. Classification report proved that it is possible to achieve very acceptable precision using semi-supervised classification to detect credit card fraud.

Article Metrics

Abstract: 84 Viewers PDF: 87 Viewers

Keywords


Data Science; Semi-Supervised Classification; Credit Card Fraud; AutoEncoders

Full Text:

PDF


References


E. Aleskerov, B. Freisleben, B. RaCARD WATCHTCH: a neural work-based database mining system for credit card fraud detection, in computational intelligence for financial engineering, Proceedings of the IEEE/IAFE, IEEE, Piscataway, NJ, 1998, pp. 220–226

CapitalOne Identity theft guide for victims, Retrieved January 10, 2009, from http://www.capitalone.com/fraud/IDTheftPackageV012172004We.pdf?linkid=WWW_Z_Z_Z_FRD_D1_01_T_FIDTP

K. Williams, The Evolution of Credit Card Fraud: Staying Ahead of the Curve,eFunds Corporation, 2007.

R.J. Bolton, D.J. Hand, Unsupervised profiling methods for fraud detecti on, Conference on Credit Scoring and Credit Control, Edinburgh, 2001.

R.J. Bolton, D.J. Hand, Statistical fraud detection: a review, Statistical Science 17(3) (2002) 235–249.

F. Provost, Comment on Bolton and Hand, Statistical Science 17 (2002) 249–251.

C. Whitrow, D.J. Hand, P. Juszczak, D. Weston, N.M. Adams, Transaction Aggregation as a strategy for credit card fraud detection, Data Mining and Knowledge Discovery 18 (1) (2009) 30–55.

N. Chawla and K. Bowyer, “SMOTE: synthetic minority oversampling technique,”arXiv preprint arXiv: . . ., vol. 16, pp. 321–357, 2011.

E. Aleskerov, B. Freisleben, and B. Rao, “Card watch: A NeuralNetwork Based Database stern for Credit Card Fraud Detection,” Proceedings of the IEEE/IAFE, pp. 220–226, 1997

J. R. Dorronsoro, F. Ginel, C. S ́anchez, and C. Santa Cruz, “Neural Fraud detection in credit card operations,” IEEE Transactions on neural networks, vol. 8, no. 4, pp. 827–834, 1997

M.-J. Kim and T.-S. Kim, “A neural classifier with fraud density map for effective credit card fraud detection,” Intelligent Data Engineering and Automated LearningIDEAL 2002, pp. 21–30.

D. Olszewski, “Fraud detection using self-organizing map visualizing the user profiles,” Knowledge-Based Systems, vol. 70, pp. 324–334,2014.

F. Ogwueleka, “Data mining application in the credit-card Fraud detection system,” Journal of Engineering Science and Technology, vol. 6, no. 3, pp. 311–322, 2011.

Y. Sahin, S. Bulkan, and E. Duman, “A cost-sensitive decision tree approach for fraud detection,” Expert Systems with Applications, vol. 40, no. 15, pp. 5916–5923, 2013

Qibei Lu; Chunhua Ju, “Research on Credit Card Fraud DetectionModel Based on Class Weighted Support Vector Machine,” Journal Of Convergence Information Technology, vol. 6, no. 1, pp. 62–68, 2011

Dheepa and R. Dhanapal, “Behavior-BasedCredit Card FraudDetection Using Support Vector Machines,” ICTACT Journal on SoftComputing, vol. 6956, no. July, pp. 391–397, 2012.

S. Jha, M. Guillen, and J. Christopher Westland, “Employing transaction aggregation strategy to detect credit card fraud,” Expert Systems withApplications, vol. 39, no. 16, pp. 12 650–12 657, 2012.

S. Panigrahi, A. Kundu, S. Sural, and a. K. Majumdar, “Credit card fraud detection: A fusion approach using Dempster-Shafer theory and bayesian learning,” Information Fusion, vol. 10, no. 4, pp. 354–363,2009.

C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, and N. M. Adams, “Transaction aggregation as a strategy for credit card fraud detection,” Data Mining and Knowledge Discovery, vol. 18, no. 1, pp. 30–55, 2009.

S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, “Data Mining for credit card fraud: A comparative study,” Decision support systems, vol. 50, no. 3, pp. 602–613, 2011.

V. Van Vlasselaer, C. Bravo, O. Caelen, T. Eliassi-Rad, L. Akoglu, M. Snoeck, and B. Baesens, “APATE: A Novel Approach for Auto-mated Credit Card Transaction Fraud Detection using Network-BasedExtensions,” Decision Support Systems, vol. 75, pp. 38–48, 2015.

C. Whitrow, D.J. Hand, P. Juszczak, D. Weston, N.M. Adams, Transaction Aggregation as a strategy for credit card fraud detection, Data Mining and Knowledge Discovery 18 (1) (2009) 30–55.

M. Imron and S. A. Kusumah, “Application of Data Mining Classification Method for Student Graduation Prediction Using K-Nearest Neighbor (K-NN) Algorithm,” IJIIS Int. J. Informatics Inf. Syst., vol. 1, no. 1, pp. 1–8, 2018, doi: 10.47738/ijiis.v1i1.17.


Refbacks

  • There are currently no refbacks.



Barcode

Journal of Applied Data Sciences

2723-6471 (Online)
Published by Bright Publisher
Puri Mersi Baru, Jl.Martadireja II, Gang Sitihingil 3 Blok A No 2, Purwokerto Timur, Jawa Tengah
Website : bright-journal.org/JADS
Email : info@bright-journal.org

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0